How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning

Abstract

In this paper, we investigate an Intelligent Tutoring System (ITS) for Java programming that implements an example-based learning approach. The approach does not require an explicit formalization of the domain knowledge but automatically identifies appropriate examples from a data set consisting of learners’ solution attempts and sample solution steps created by experts. In a field experiment conducted in an introductory course for Java programming, we examined four example selection strategies for selecting appropriate examples for feedback provision and analyzed how learners’ solution attempts changed depending on the selection strategy. The results indicate that solutions created by experts are more beneficial to support learning than solution attempts of other learners, and that examples modeling steps of problem solving are more appropriate for very beginners than complete sample solutions.